{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:45:32Z","timestamp":1776437132169,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T00:00:00Z","timestamp":1543536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41501485"],"award-info":[{"award-number":["41501485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection plays an important role in automatic remote sensing image interpretation. The scale difference, large aspect ratio of ship, complex remote sensing image background and ship dense parking scene make the detection task difficult. To handle the challenging problems above, we propose a ship rotation detection model based on a Feature Fusion Pyramid Network and deep reinforcement learning (FFPN-RL) in this paper. The detection network can efficiently generate the inclined rectangular box for ship. First, we propose the Feature Fusion Pyramid Network (FFPN) that strengthens the reuse of different scales features, and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships. Second, in order to get accurate ship angle information, we apply deep reinforcement learning to the inclined ship detection task for the first time. In addition, we put forward prior policy guidance and a long-term training method to train an angle prediction agent constructed through a dueling structure Q network, which is able to iteratively and accurately obtain the ship angle. In addition, we design soft rotation non-maximum suppression to reduce the missed ship detection while suppressing the redundant detection boxes. We carry out detailed experiments on the remote sensing ship image dataset, and the experiments validate that our FFPN-RL ship detection model has efficient detection performance.<\/jats:p>","DOI":"10.3390\/rs10121922","type":"journal-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T12:13:17Z","timestamp":1543579997000},"page":"1922","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning"],"prefix":"10.3390","volume":"10","author":[{"given":"Kun","family":"Fu","sequence":"first","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9271-2977","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6287-3590","authenticated-orcid":false,"given":"Xue","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangluan","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Li","sequence":"additional","affiliation":[{"name":"College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,30]]},"reference":[{"key":"ref_1","first-page":"53","article-title":"A Method for Automatic Detection of Ships in Harbor Area in High-Resolution Remote Sensing Image","volume":"5","author":"Long","year":"2007","journal-title":"Comput. Simul."},{"key":"ref_2","first-page":"88","article-title":"An AIAC-based Inshore Ship Target Detection Approach","volume":"22","author":"Jiang","year":"2007","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/LGRS.2013.2272492","article-title":"A New Method on Inshore Ship Detection in High-Resolution Satellite Images Using Shape and Context Information","volume":"11","author":"Liu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","first-page":"84","article-title":"A Method for Discrimination of Ship Target and Azimuth Ambiguity in Multi-polarimetric SAR Imagery","volume":"4","author":"Zhou","year":"2015","journal-title":"J. Radars"},{"key":"ref_5","first-page":"367","article-title":"Ship Analysis and Detection in High-resolution Pol-SAR Imagery Based on Peak Zone","volume":"4","author":"Xu","year":"2015","journal-title":"J. Radars"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/LGRS.2017.2688357","article-title":"Integrated Localization and Recognition for Inshore Ships in Large Scene Remote Sensing Images","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2011.2180695","article-title":"A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images","volume":"9","author":"Bi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/LGRS.2015.2408355","article-title":"Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor From Optical Satellite Images","volume":"12","author":"Qi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dao, M., Kwan, C., Koperski, K., and Marchisio, G. (2018, January 8\u201310). A Joint Sparsity Approach to Tunnel Activity Monitoring Using High Resolution Satellite Images. Proceedings of the 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, New York, NY, USA.","DOI":"10.1109\/UEMCON.2017.8249061"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral Image Classification Using Dictionary-Based Sparse Representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.aasri.2012.06.077","article-title":"Moving-object Detection Based on Sparse Representation and Dictionary Learning","volume":"1","author":"Huang","year":"2012","journal-title":"Aasri Procedia"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xiao, Q., Hu, X., Gao, S., and Wang, H. (2010, January 7\u20139). Object Detection Based on Contour Learning and Template Matching. Proceedings of the 2010 8th World Congress on Intelligent Control and Automation, Jinan, China.","DOI":"10.1109\/WCICA.2010.5554344"},{"key":"ref_13","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_14","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (arXiv, 2014). Going Deeper with Convolutions, arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (arXiv, 2016). Identity Mappings in Deep Residual Networks, arXiv.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (arXiv, 2016). Densely Connected Convolutional Networks, arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201322). Learning Transferable Architectures for Scalable Image Recognition. Proceedings of the 2018 Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_20","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T., Dollar, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nguyen, P., Arsalan, M., Koo, J., Naqvi, R., Truong, N., and Kang, P. (2018). LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone. Sensors, 18.","DOI":"10.3390\/s18061703"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-Oriented Scene Text Detection via Rotation Proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., and Luo, Z. (arXiv, 2017). R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection, arXiv.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks. Remote Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_28","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M.A. (arXiv, 2013). Playing Atari with Deep Reinforcement Learning, arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Van Hasselt, H., Guez, A., and Silver, D. (2016, January 12\u201317). Deep Reinforcement Learning with Double Q-Learning. Proceedings of the AAAI, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"ref_31","unstructured":"Wang, Z., Schaul, T., Hessel, M., Van Hasselt, H., Lanctot, M., and De Freitas, N. (2016, January 19\u201324). Dueling network architectures for deep reinforcement learning. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_32","unstructured":"Kulkarni, T.D., Narasimhan, K., Saeedi, A., and Tenenbaum, J.B. (2016, January 5\u201310). Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. Proceedings of the Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_33","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D.P. (2016, January 2\u20133). Continuous control with deep reinforcement learning. Proceedings of the International Conference on Learning Representations, San Juan, PR, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yun, S., Choi, J., Yoo, Y., Yun, K., and Choi, J.Y. (2017, January 21\u201326). Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.148"},{"key":"ref_35","unstructured":"Lan, X., Wang, H., Gong, S., and Zhu, X. (arXiv, 2017). Identity Alignment by Noisy Pixel Removal, arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J., Lam, V., Bewley, A., and Shah, A. (arXiv, 2018). Learning to Drive in a Day, arXiv.","DOI":"10.1109\/ICRA.2019.8793742"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_39","unstructured":"Quek, A. (2018, November 27). Ship Orientation Estimation. Available online: https:\/\/alyssaq.github.io\/2015\/computing-the-axes-or-orientation-of-a-blob\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1922\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:33:35Z","timestamp":1760196815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,30]]},"references-count":39,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10121922"],"URL":"https:\/\/doi.org\/10.3390\/rs10121922","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,30]]}}}